code: def __init__(self, dim, dim_head=64, heads=8):
      super().__init__()
      inner_dim = dim_head * heads
      self.norm = LayerNorm(dim)
      self.heads = heads
      self.scale = dim_head**-0.5
      self.rotary_emb = RotaryEmbedding(dim_head)
  
      self.to_q = nn.Linear(dim, inner_dim, bias=False)
      self.to_kv = nn.Linear(dim, dim_head * 2, bias=False)
      self.to_out = nn.Linear(inner_dim, dim, bias=False)
  
      # for caching causal mask and rotary embeddings
  
      self.register_buffer("mask", None, persistent=False)
      self.register_buffer("pos_emb", None, persistent=False)
  
imports: import torch
  import torch.nn.functional
  from einops import rearrange
  from torch import matmul, nn
label_desc: Use image node_id 1 to create a new Attention object from inherited base classes: nn with example: obj = Attention(dim, dim_head, heads)